Titlebook: Game Theory for Data Science; Eliciting Truthful I Boi Faltings,Goran Radanovic Book 2017 The Editor(s) (if applicable) and The Author(s),

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Decentralized Machine Learning,l. This involves consideration of how the information agents are found and how they interact with the center, and how the center aggregates the data it receives into a model. In this chapter, we discuss issues that present themselves when designing the complete system so that the assumptions of the

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Conclusions, designer. Since obtaining accurate data is costly, data providers need to be compensated for their effort. This requires that we can evaluate the quality of the data so that only accurate data is paid.

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,Biochemistry of Host–Pathogen Interaction, to it. However, this confidence must also be elicited in some way—agents may want to have a strong influence and thus overclaim their influence. The idea of a . is to make agents take . in proportion to their confidence, so that having a larger influence on the aggregate information requires taking a larger risk.

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https://doi.org/10.1007/978-1-4899-3440-6l. This involves consideration of how the information agents are found and how they interact with the center, and how the center aggregates the data it receives into a model. In this chapter, we discuss issues that present themselves when designing the complete system so that the assumptions of the incentive mechanisms are satisfied.

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Mechanisms for Verifiable Information,. This case is actually quite frequent. It includes forecasts of weather, product sales, election outcomes and many other phenomena. Sometimes environmental measurements, such as pollution, can be verified on a cumulative basis at low cost. Crop diseases eventually become apparent.

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Prediction Markets: Combining Elicitation and Aggregation, to it. However, this confidence must also be elicited in some way—agents may want to have a strong influence and thus overclaim their influence. The idea of a . is to make agents take . in proportion to their confidence, so that having a larger influence on the aggregate information requires taking a larger risk.

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Decentralized Machine Learning,l. This involves consideration of how the information agents are found and how they interact with the center, and how the center aggregates the data it receives into a model. In this chapter, we discuss issues that present themselves when designing the complete system so that the assumptions of the incentive mechanisms are satisfied.

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